Mobile Manipulator Obstacle Avoidance With Dual Embedded Control
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Industrial robots face challenges in real-time obstacle avoidance due to poor response speed and reliability, particularly when operating at high speeds, leading to potential collisions and production disruptions in manufacturing environments.
Innovation Solution
A dual embedded platform architecture is employed, where a first embedded platform processes real-time obstacle avoidance algorithms and generates data, which is immediately transmitted to a second embedded platform for motor control, while the robot control platform handles overall decision-making, ensuring rapid and reliable obstacle avoidance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a robot control platform based on x86 platform structure is used to perform complex calculations for obstacle recognition and avoidance decisions, then the robot can make accurate obstacle avoidance decisions, but the real-time response speed deteriorates and computing resources are tightly consumed
Solution Approach 1:
The system divides the control platform into two independent embedded platforms: the first embedded platform dedicated to real-time obstacle detection and avoidance decision-making, and the second embedded platform dedicated to motion control execution. This segmentation allows each platform to specialize in specific tasks, with the first platform achieving fast real-time response for obstacle detection and the second platform ensuring precise motion control, thereby resolving the contradiction between decision accuracy and response speed.
2Productivity
If the robot travels at high speed to meet production rhythm requirements, then productivity is improved, but the robot's ability to recognize and avoid obstacles in real-time deteriorates
Solution Approach 1:
The first embedded platform continuously and proactively monitors the environment for obstacles before the robot encounters them, performing preliminary detection and decision-making. This allows the system to prepare avoidance decisions in advance, ensuring that even when traveling at high speeds to meet production requirements, the robot can reliably recognize and respond to obstacles without compromising safety.
3Measurement precision
If complex calculations are performed on the robot control platform to obtain perception results, then accurate obstacle recognition is achieved, but computing resources are tightly consumed and response time increases
Solution Approach 1:
The system extracts the real-time obstacle detection and decision-making functions from the general-purpose robot control platform and implements them on a dedicated first embedded platform. This extraction allows complex calculations for obstacle recognition to be performed on specialized hardware optimized for real-time processing, reducing calculation time and resource consumption while maintaining accurate obstacle detection capabilities.
Data Source
Figure 1~3
Figure 4
Figure 5
AI summary
Provided by the present invention are a mobile operation robot obstacle avoidance system and a real-time obstacle avoidance method. A first embedded platform is configured for acquiring first sensing data output by a sensor, and executing a real-time obstacle avoidance algorithm to generate obstacle avoidance data when acquiring the first sensing data. The robot control platform is configured for acquiring all sensing data output by the sensor and the obstacle avoidance data so as to execute an overall control function of the robot and generate decision data. The second embedded platform is configured for immediately executing a robot motion control algorithm upon receiving the obstacle avoidance data so as to output an action instruction required for obstacle avoidance according to the obstacle avoidance data, to enable a motor configured for performing motion action in the robot to execute the action instruction to complete real-time obstacle avoidance; and executing the robot motion control algorithm upon receiving the decision data so as to drive the motor to perform motion action adjustment according to the decision data. Therefore, the obstacle avoidance reliability and response speed of the robot are improved, and the requirements of industrial scenarios are met.